BACKGROUND: Users of electronic health record (EHR) systems frequently prescribe doses outside recommended dose ranges, and tend to ignore the alerts that result. Since some of these dosing errors are the result of system design flaws, analysis of large overdoses can lead to the discovery of needed system changes. OBJECTIVES: To develop database techniques for detecting and extracting large overdose orders from our EHR. To identify and characterize users' responses to these large overdoses. To identify possible causes of large-overdose errors and to mitigate them. METHODS: We constructed a data mart of medication-order and dosing-alert data from a quaternary pediatric hospital from June 2011 to May 2013. The data mart was used along with a test version of the EHR to explain how orders were processed and alerts were generated for large (>500%) and extreme (>10,000%) overdoses. User response was characterized by the dosing alert salience rate, which expresses the proportion of time users take corrective action. RESULTS: We constructed an advanced analytic framework based on workflow analysis and order simulation, and evaluated all 5,402,504 medication orders placed within the 2 year timeframe as well as 2,232,492 dose alerts associated with some of the orders. 8% of orders generated a visible alert, with ¼ of these related to overdosing. Alerts presented to trainees had higher salience rates than those presented to senior colleagues. Salience rates were low, varying between 4-10%, and were lower with larger overdoses. Extreme overdoses fell into eight causal categories, each with a system design mitigation. CONCLUSIONS: Novel analytic systems are required to accurately understand prescriber behavior and interactions with medication-dosing CDS. We described a novel analytic system that can detect apparent large overdoses (≥500%) and explain the sociotechnical factors that drove the error. Some of these large overdoses can be mitigated by system changes. EHR design should prospectively mitigate these errors.
BACKGROUND: Users of electronic health record (EHR) systems frequently prescribe doses outside recommended dose ranges, and tend to ignore the alerts that result. Since some of these dosing errors are the result of system design flaws, analysis of large overdoses can lead to the discovery of needed system changes. OBJECTIVES: To develop database techniques for detecting and extracting large overdose orders from our EHR. To identify and characterize users' responses to these large overdoses. To identify possible causes of large-overdose errors and to mitigate them. METHODS: We constructed a data mart of medication-order and dosing-alert data from a quaternary pediatric hospital from June 2011 to May 2013. The data mart was used along with a test version of the EHR to explain how orders were processed and alerts were generated for large (>500%) and extreme (>10,000%) overdoses. User response was characterized by the dosing alert salience rate, which expresses the proportion of time users take corrective action. RESULTS: We constructed an advanced analytic framework based on workflow analysis and order simulation, and evaluated all 5,402,504 medication orders placed within the 2 year timeframe as well as 2,232,492 dose alerts associated with some of the orders. 8% of orders generated a visible alert, with ¼ of these related to overdosing. Alerts presented to trainees had higher salience rates than those presented to senior colleagues. Salience rates were low, varying between 4-10%, and were lower with larger overdoses. Extreme overdoses fell into eight causal categories, each with a system design mitigation. CONCLUSIONS: Novel analytic systems are required to accurately understand prescriber behavior and interactions with medication-dosing CDS. We described a novel analytic system that can detect apparent large overdoses (≥500%) and explain the sociotechnical factors that drove the error. Some of these large overdoses can be mitigated by system changes. EHR design should prospectively mitigate these errors.
Entities:
Keywords:
CPOE; Electronic health record; clinical decision support systems; electronic medical record; medical order entry system
Authors: Thomas Isaac; Joel S Weissman; Roger B Davis; Michael Massagli; Adrienne Cyrulik; Daniel Z Sands; Saul N Weingart Journal: Arch Intern Med Date: 2009-02-09
Authors: Heather A McPhillips; Christopher J Stille; David Smith; Julia Hecht; John Pearson; John Stull; Kristin Debellis; Susan Andrade; Marlene Miller; Rainu Kaushal; Jerry Gurwitz; Robert L Davis Journal: J Pediatr Date: 2005-12 Impact factor: 4.406
Authors: Sharon Conroy; Dimah Sweis; Claire Planner; Vincent Yeung; Jacqueline Collier; Linda Haines; Ian C K Wong Journal: Drug Saf Date: 2007 Impact factor: 5.606
Authors: Barbara Maat; Yuen San Au; Casper W Bollen; Adrianus J van Vught; Toine C G Egberts; Carin M A Rademaker Journal: Arch Dis Child Date: 2012-11-26 Impact factor: 3.791
Authors: Judith W Dexheimer; Eric S Kirkendall; Michal Kouril; Philip A Hagedorn; Thomas Minich; Leo L Duan; Monifa Mahdi; Rhonda Szczesniak; S Andrew Spooner Journal: Appl Clin Inform Date: 2017-05-10 Impact factor: 2.342
Authors: E Borycki; J W Dexheimer; C Hullin Lucay Cossio; Y Gong; S Jensen; J Kaipio; S Kennebeck; E Kirkendall; A W Kushniruk; C Kuziemsky; R Marcilly; R Röhrig; K Saranto; Y Senathirajah; J Weber; H Takeda Journal: Yearb Med Inform Date: 2016-11-10
Authors: Mitchell S Buckley; Jeffrey R Rasmussen; Dale S Bikin; Emily C Richards; Andrew J Berry; Mark A Culver; Ryan M Rivosecchi; Sandra L Kane-Gill Journal: Ther Adv Drug Saf Date: 2018-03-01
Authors: Kinjal Gadhiya; Edgar Zamora; Salim M Saiyed; David Friedlander; David C Kaelber Journal: Appl Clin Inform Date: 2021-04-28 Impact factor: 2.342
Authors: Eric S Kirkendall; Michal Kouril; Judith W Dexheimer; Joshua D Courter; Philip Hagedorn; Rhonda Szczesniak; Dan Li; Rahul Damania; Thomas Minich; S Andrew Spooner Journal: J Am Med Inform Assoc Date: 2017-03-01 Impact factor: 4.497